Cooperative localization for wireless networks

ABSTRACT

A system and corresponding method using a cooperative localization technique for self identifying a location of a wireless device in a wireless network is presented. The system may estimate an arbitrary signal metric as a function of a signal received by the wireless device from the at least one other wireless device in the wireless network. The system may also convert at least one belief representing a distribution of at least one possible location of the at least one other wireless device to generate at least one converted belief. The system may further determine a self-belief as a function of the at least one converted belief and identify a self location, as a function of the self-belief, within the wireless network.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/854,393, filed on Oct. 25, 2006, entitled “COOPERATIVE LOCALIZATIONFOR UWB NETWORKS.” The entire teachings of the above application areincorporated herein by reference.

GOVERNMENT SUPPORT

The invention was supported, in whole or in part, by the NationalScience Foundation under Grant Nos. ECCS-0636519 and ANI-0336518, andthe Office of Naval Research under Grant No. N00014-03-1-0489. TheGovernment has certain rights in the invention.

BACKGROUND OF THE INVENTION

Location-aware technologies have the potential to revolutionizecomputing, cellular services, sensor networks, and many othercommercial, military, and social applications. Localization may be usedto find the location of wireless devices in a network. The network mayinclude any number of wireless devices, known as agents, as well as anumber of anchors. Anchors (e.g., cellular base stations) may typically

One known technique for determining the location of agents in a networkis centralized localization. In centralized localization, each node(e.g., anchors and agents) in the network routes its collection ofrelative positioning measurements to a central processing unit. Thecentral processing unit may determine the locations of all the nodes inthe network using a set of measurements obtained from each node, forexample, by employing an optimization function over the set ofmeasurements.

The majority of current localization methods utilize another knownlocalization technique referred to as non-cooperative localization. In anon-cooperative localization approach, there is no communication betweenagents; there only exists communication between agents and anchors.During the localization process, an agent may need to be incommunication range with as many as three anchors in order to determineits location. Thus, in order for an agent to locate itself, a number ofanchors may need to be employed in order to ensure each agent has accessto enough data to determine its location. Thus, to locate all agentsnon-cooperative localization may require either a high density ofanchors or long-range high-power anchor transmissions.

A third known technique of localization is cooperative localization. Incooperative localization, inter-agent communication is allowed, removingthe need for all agents to be within communication range of multipleanchors. Therefore, agents may act as virtual anchors for other agents.Since every agent has more information available, cooperativelocalization may offer more accuracy and coverage.

SUMMARY OF THE INVENTION

Although prior art methods of cooperative localization offerimprovements over current centralized and non-cooperative localizationmethods, these prior art cooperative localization methods still includea number of drawbacks. Prior art cooperative localization methodstypically allow only the exchange of a single position estimate fromnode to node. The use of single estimates may introduce inaccuracies inlocalization computation. Furthermore, prior art methods of cooperativelocalization only make use of symmetrical data sets, such asnode-to-node distance estimations. Prior art methods of cooperativelocalization also require that all the nodes in the network behomogenous, where processed signal metrics, such as received signalstrength, may be obtained by nodes in communication with each other thatare of an equal type. Also, in prior art methods of cooperativelocalization, it is typically necessary that each node use the sameprocess, or algorithm, of estimating the signal metrics, thereby makingit necessary that each node include identical hardware and softwarecapabilities.

In an example embodiment of the present invention, a cooperativelocalization technique is utilized to obtain distributions of possiblelocations for the agents in the network system by allowing agents andanchors to transmit probabilities of their possible locations. In anembodiment of the present invention, heterogeneous nodes andheterogeneous processing methods may be employed to process symmetricaland non-symmetrical data sets, which may greatly improve localizationcapabilities. In another embodiment of the present invention, factorgraph theory may be used to develop a fully cooperative localizationmethod to determine an estimated position of all the nodes in thenetwork.

In an embodiment of the present invention, a system and correspondingmethod for self identifying a location of a wireless device in awireless network are described. The system may comprise a calculatingunit configured to calculate at least one converted belief representinga distribution of at least one possible location of the wireless devicewithin the wireless network. The at least one converted belief may be afunction of an arbitrary signal metric associated with a wireless signalreceived by the wireless device from at least one other wireless device.The distribution may be a probability density or a mass function. Thesystem may further include a belief determining unit configured todetermine a self-belief as a function of the at least one convertedbelief, and an identifying unit configured to identify a self location,as a function of the self-belief, within the wireless network.

The wireless network may be time varying. Specifically, at least asubset of multiple other wireless devices may be mobile relative to atleast one other subset of multiple other wireless devices, andrespective estimating, converting, belief determining, and identifyingunits may be further configured to account for mobility.

The respective estimating units of at least a subset of multiple otherwireless devices may be configured to estimate the arbitrary signalmetric of a first type, and respective estimating units of at least oneother subset of the multiple other wireless devices may be configured toestimate the arbitrary signal metric of a second type. The arbitrarysignal metric may be a characteristic of the wireless device withrespect to the at least one other wireless device, and thecharacteristic may be selected from a group consisting of: angle,distance, connectivity, position, pose, velocity, and angular velocityinformation. The characteristic corresponding to an arbitrary signalmetric of the first type may be non-symmetrical with respect to thecharacteristic corresponding to an arbitrary signal metric of the secondtype.

The converting unit may be further configured to convert the at leastone belief, and the determining unit may be configured to determine theself-belief, based on factor graph principles. The converting unit mayalso be further configured to self account for a capability related toestimating the arbitrary signal metric.

The determining unit may be further configured to update a previouslydetermined self-belief or determine a new self-belief. The identifyingunit may be further configured to identify a location of at least oneother wireless device in the wireless network.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of example embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingembodiments of the present invention.

FIGS. 1 and 2 are overhead diagrams of environments in which examplelocalization methods and corresponding apparatus according to thepresent invention may be employed;

FIGS. 3A-3C are diagrams illustrating examples of attributes of relativepositions (ARP) between a wireless device and at least one otherwireless device;

FIG. 4 is a diagram of an example of a factor graph representing thenetwork shown in FIG. 2;

FIG. 5 is a schematic diagram of an example location processor employedin the nodes of FIG. 2; and

FIG. 6 is a flow diagram depicting example operations of a cooperativelocalization method according an example embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

A description of example embodiments of the invention follows.

FIG. 1 provides an example of a floor plan of an indoor environment 100featuring various mobile devices, or agents 103 a-103 h, employing acentralized location method. The agents may be, for example, wirelessdevices associated with individual firemen during a search and rescuemission. The wireless devices may be mobile and therefore have anunknown location at times. The wireless devices may solely rely on“anchors” (e.g., wireless access points) 105 a-105 c and a centrallocalization computation unit 109 to determine their respectivelocation. The anchors may be placed in fixed and known locations. Theanchors may transmit and receive signals from nearby agents in anattempt to provide a location for the various agents based on distance(e.g., the distance from the anchor to agent) estimations. The anchorsmay also be configured to be in communication with the centrallocalization computation unit 109 via connections 107, which may bewireless. In order for an agent to determine its location via acentralized localization method, the various anchors may sendinformation receive from the agents to the central localizationcomputation unit. The central unit 109 may compute the estimatedlocation of each agent in the wireless network. While the centralizedlocalization method may be used to produce reliable locationestimations, this method has a drawback of requiring all thecomputations to be performed in a single location.

FIG. 2 illustrates an example of a network utilizing a non-cooperativelocalization method. The network 200 includes three cellular BaseTransceiver Stations (“base stations”) (BTS 3, BTS 4, and BTS 5), whichmay serve as anchors. The cellular base stations, or anchors, may eachhave knowledge of their respective locations. The network may alsoinclude a number of mobile stations (MS), such as cellular phones (e.g.,MS 1 and MS 2), which may serve as agents. The cellular phones, oragents, may have little or no knowledge of their respective location.Furthermore, the agents may also be mobile throughout the network;therefore, the agents may have a variable location. It should beappreciated that anchors and agents are not limited to base stations andcellular phones, respectively, but may be any form of wireless device,where base stations are included in this definition of wireless device,even though a base station is typically wired to a network. It shouldalso be appreciated that both the anchors and agents may be mobile.

In the non-cooperative method of localization, the agents onlycommunicate with the anchors in determining their location in thenetwork. For example, consider the case where agent MS 1 is located in aposition in the network where it is only capable of communicating withanchors BS 3 and BS 5 (i.e., agent MS 1 may not be in range tocommunicate with anchor BS 4). Therefore, upon communicating with anchorBS 3, agent MS 1 may obtain an information packet (not shown) from theanchor. In the example provided by FIG. 2, the wireless signalcorresponding to the information packet may provide a signal metric ofan estimate of an attribute of a relative position (ARP) of agent MS 1to anchor BS 3.

Signal metrics may be based on one or several factors, such as: time ofarrival, time difference of arrival, received signal strength, angle ofarrival, Doppler shifts, and connectivity. These signal metrics mayprovide information about a relative position (ARP) between respectivetransmitters and receivers. For example, to estimate the ARP ofdistance, the signal metrics time of arrival, time difference of arrive,roundtrip time of flight, received signal strength, and hop-count may beused. To estimate the ARP of angle, the signal metric angle of arrivalmay be used or information representing antenna steering may be used. Toestimate the ARP of connectivity, the signal metric received signalstrength (RSS) may be used.

FIGS. 3A-3C provide example forms of the ARP information, which maydescribe the connectivity (FIG. 3A; constraining the possible locationsof the agent MS 1 to be within an area of overlapping discs, each dischaving a known radius around anchors BS 3 and BS 5), distance (FIG. 3B;constraining the possible locations of the agent MS 1 to be on a portionof overlapping circumferences of circles, with each circle having aknown radius around anchor BS 3 and BS 5, respectively), or the angle(FIG. 3C; constraining the possible locations of the agent MS 1 to be ona set of lines departing from anchor BS 3 or BS 5). Various other ARPsmay be obtained based on signal metric data, such as ARPs describingposition, pose, velocity, and angular velocity information for a node.

The ARP of distance is symmetrical. For example, the distance from nodeA to node B is equal to the distance from node B to node A. The ARPs ofangle and connectivity may be asymmetrical, since the angle orconnectivity from node A to node B may not be equal to the angle orconnectivity from node B to node A.

In the example provided by FIG. 2, the signal metric of received signalstrength may be used to estimate the symmetrical ARP of distance. Giventhe ARP estimate, agent MS 1 may determine that it is located at aparticular distance, or radius, from the anchor BS 3. The possiblelocations of the agent MS 1, given the information provided by theanchor BS 3, may be represented by a dotted circle 201.

The agent MS 1 may obtain distance information from the anchor BS 5,resulting in a dotted circle 202 around anchor BS 5, representing allpossible locations of MS 1 with respect to the anchor BS 5. Hence, theactual location of the agent MS 1 may be limited to the intersection ofthe two dotted circles 201 and 202. Therefore, this may result in twopossible locations 203 and 205 for the agent MS 1. Since agent MS 1 isnot within a communication range of three anchors, MS 1 cannot uniquelydetermine its own position.

Agent MS 2 may also not be able to uniquely determine its own positionsince agent MS 2 is only within communication range of anchors BS 4 andBS 5. The possible locations 207 and 209 of agent MS 2 correspond to theintersection of dotted circles 206 and 208.

Another method of determining positions of wireless devices may be bymeans of cooperative localization. In cooperative localization, agentsmay communicate both with anchors as well with other agents byexchanging their position estimates. Thus, agents may function asvirtual anchors. Agents and anchors may be considered as nodes, whereeach node may have an ability to communicate with other nodes,regardless of their type. Inter-agent communication removes the need forall agents to be within range of one or more anchors.

However, prior art methods of cooperative localization include a numberof drawbacks. Prior art methods of cooperative localization allow only asingle location estimate to be sent at a time. Prior art methods ofcooperative localization are also limited in that the prior art methodsonly allow for the use of symmetrical ARPs (e.g., distance basedmeasurements). Furthermore, prior art methods of cooperativelocalization typically require that all the nodes of the network beheterogeneous, where each node receives and transmits signals resultingin ARPs of a same type. The heterogeneous nodes also require similarhardware and software capabilities to allow the location estimations tobe processed in a similar manner.

In an example embodiment of the present invention, cooperativetechniques are utilized to obtain possible locations for the agents inthe network system by allowing agents and anchors to transmitdistributions characterizing the probabilities of possibly multiplelocations. In an embodiment of the present invention, signal metrics maybe used to obtain ARPs that are symmetrical and asymmetrical. Thus, thecooperative localization method, according to embodiments of the presentinvention, may employ any ARP known in the art and are not limitedsolely to the ARP of distance. It should also be appreciated that anysignal metric and ARP known in the art may be employed in any exampleembodiments of the present invention.

In another embodiment of the present invention, the nodes of the systemmay be heterogeneous, therefore allowing each node to estimate alocation by utilizing ARPs that may be non-symmetrical, have unequalvalues, and be of a different type. The heterogeneous nodes of thenetwork, according to an example embodiment of the present invention,may also include different hardware and software systems from node tonode. In another embodiment of the present invention, factor graphtheory may be used to develop a fully cooperative localization method todetermine an estimated position of all the nodes in the network.

FIG. 4 is a top view diagram that illustrates a factor graphrepresentation of the network shown in FIG. 2. FIG. 5 represents aschematic of a location processor which may be found in each agent ofFIG. 2. FIG. 6 is a flow diagram illustrating possible operations of anexample cooperative localization method. FIGS. 4-6 describe amathematical, hardware, and software approach, respectively, tocooperative localization according to example embodiments of the presentinvention.

Factor graphs may provide an intuitive way to represent and understandmultivariable functions. A factor graph may express a global function asthe product of factors, or local functions. By illustrating whichfactors depend on which variables, a factor graph shows how thevariables of the global function are interdependent through shared localfunctions.

In an example embodiment of the present invention, a factor graph may beobtained from a joint a posteriori probability distribution p(x₁, . . ., x_(N)|Z), representing possible positions of all the nodes in thenetwork given the collection of signal metrics Z, with N being equal tothe total number of nodes in the network. The joint a posterioridistribution may be expressed as:

${{p( {xz} )} \propto {{p( {zx} )}{p(x)}}} = {\prod\limits_{i = 1}^{N}{\prod\limits_{j \in \Gamma_{i}}{{p( {{z_{j->i}x_{i}},x_{j}} )}{p( x_{i} )}}}}$

where z_(j→i) may be a signal metric obtained by node i from a signaltransmitted by node j. The function p(z_(j→i)|x_(i), x_(j)) is thedistribution of the signal metric conditioned on the locations of nodesi and j.

Upon obtaining expression for the joint a posteriori probabilitydistribution p(x₁, . . . , x_(N)|Z), a sum-product algorithm (SPA), alsoknown as belief propagation, may be used on the created factor graph todetermine approximations of marginal a posteriori distributionsp(x_(i)|Z) for all nodes i. These approximations of the marginal aposteriori distribution may be used by every node in the network toestimate its own position x_(i). These approximations of the marginal aposteriori distributions p(x_(i)|Z) may also be mapped to the physicalnetwork using factor graph theory, which is described in more detailbelow.

In the example provided by FIG. 4, the five nodes BS 3, BS 5, BS 4, MS1, and MS 2, of the network of FIG. 2, may be associated with sub-graphshighlighted by boxes 403, 415, 411, 407, and 419, respectively. Thepossible locations of nodes MS 1, MS 2, BS 3, BS 4, and BS 5 may berepresented as edges in the factor graph labeled x₁-x₅, respectively.The possible locations of the agent nodes MS 1 and MS 2 (x₁ and x₂,respectively) may be provided by information sent by nearby nodes. Theoperations of obtaining the possible locations may be represented in thefactor graph 400 by vertices, which are described in more detail below.

In an example embodiment of the present invention, the first operationof the cooperative localization method may be to initialize the beliefof all the nodes in the network (601). In the initialization, the beliefof the anchors and agents may be set equal to their respective marginala priori distributions p(x_(i)). The marginal a priori distributionp(x_(i)) is a function of the variable, x_(i), for the possiblepositions of node i. The marginal a priori p(x_(i)) distribution mayreflect the knowledge node i may have before the start of thelocalization process. The marginal a priori distribution for each nodein the network BS 3, BS 5, BS 4, MS 1, and MS 2 are labeled as vertices401, 413, 409, 405, and 417, respectively.

Since the anchors BS 3, BS 5, and BS 4 have knowledge of theirrespective locations, the anchors may initialize their beliefs to an apriori distribution in the form of a Dirac delta distribution. A Diracdelta distribution is a function defining a non-zero probability foronly one position, which is the known location for the respectiveanchor. It should be appreciated that there may be anchors in thenetwork who may not have full knowledge of their location at all times;therefore, the a priori distribution of an anchor with partial knowledgeof its location may be in the form of a distribution including anarbitrary shape.

The agents MS 1 and MS 2 may initialize their beliefs to an a prioridistribution in the form of a uniform distribution. The uniformdistribution may have an equal probability value for all possiblelocations of the respective agent, since the agent may not initiallyhave knowledge of its self location. The agents may also have partialknowledge of their location, thereby causing the a priori distributionto have an arbitrary shape. The agents may also have full knowledge oftheir location, which results in an a priori distribution defined by aDirac delta distribution.

It should be appreciated that the agents and anchors may have any of theabove mentioned distributions as well as any other distributions knownin the art. It should also be appreciated that agents and anchors neednot all have the same distributions as there may be some agents andanchors who have more knowledge of their respective locations incomparison to other agents and anchors.

In one embodiment, upon initializing the beliefs of all the nodes in thenetwork, an iteration value may be set or determined (603). Theiteration value may define the number of times the cooperativelocalization method may be executed by each node in the network. Themaximum iteration value ‘max’ may be set dynamically to the run-time ofthe network. Thus, the network may be configured to track constantly andlocalize the various devices associated with the network. It should beappreciated that not all nodes may take part in every iteration.

Once an iteration has been defined, the system may continually orcontinuously monitor a current value of the iteration ‘L’ to determineif the maximum iteration value ‘max’ has been reached (605). If it isdetermined that the maximum iteration value ‘max’ has been reached, thelocalization system may be directed to come to an end (607). Otherwise,the nodes in the network, or node system, may proceed with an iterationof the cooperative localization (609). First, the system may beconfigured to update the self-beliefs of all the nodes in the network(611). The self-belief updating may be based on the mobility of thenodes, and may rely on any technique known in the art, such as aninertial measurement unit or gyroscope. Both the agents and the anchorsmay have the ability to be mobile. It should be appreciated that, in atleast one embodiment, not all nodes in the network are updated;therefore, only a subset of the nodes are updated at a time in such anembodiment. Thus, the marginal a priori distributions p(x_(i)) of eachnode BS 3, BS 5, BS 4, MS 1, and MS 2, labeled as vertices 401, 413,409, 405, and 417, respectively, may be updated, accordingly.

Furthermore, during this time, new nodes may be detected as entering thenetwork or current nodes may be detected as departing the network, thuschanging the configuration of the factor graph (e.g., new sub-graphscorresponding to nodes entering the network may be added to the factorgraph, or sub-graphs of nodes departing the network may be removed).Thus, the topology of the factor graph may change as the topology of thenetwork changes throughout the lifetime of the cooperative localizationprocess.

Next, all the nodes in the network may broadcast their currentself-belief (613). This broadcasting may be achieved with the use of anSPA. The SPA may be applied as message passing on the factor graph toobtain approximations of the marginal a posteriori distributionsp(x_(i)|Z) for each node in the network. The marginal a posterioridistribution p(x_(i)|Z) may be approximated by the self-belief b(x_(i)).

For example, in the factor graph shown in FIG. 4, the anchor BS 3 maytransmit its self-belief, b(x₃) 421, to agent MS 1, since agent MS 1 isin communication range of anchor BS 3. Similarly, agent MS 1 may act asa virtual anchor and transmit its self-belief, b(x₁) 423, to agent MS 2,since agent MS 1 is within communication range of agent MS 2. Likewise,agent MS 2 may also act as a virtual anchor and transmit itsself-belief, b(x₂) 425, to agent MS 1. The anchor BS 4 is incommunication range of agent MS 2 and may therefore transmit itsself-belief, b(x₄) 427, to agent MS 2. The anchor BS 5 is incommunication range to both agent MS 1 and agent MS 2, and may thereforetransmit its self-belief, b(x₅) 428 and 429, to agent MS 1 and agent MS2, respectively.

In the sub-graph 415 associated with anchor BS 5, an equality vertex 431may be used when sending self-beliefs to multiple nodes. Similarly, thesub-graphs 407 and 419 of agents MS 1 and MS 2, respectively, alsoinclude equality vertices 431 since the agents are in communication withmultiple nodes.

It should be appreciated that a node may send and receive multiplemessage packets during the broadcast period. For example, if node Aestimates the signal metric of round-trip time, node A may send a packetto node B, and node B may send a packet back to node A.

Upon the self-belief broadcast of each node in the network, each nodemay individually employ a location processor 500 to compute a newself-belief based on the broadcasted beliefs. First, a node may receivethe broadcasted beliefs 501 from other nodes in the network via anantenna 503 (617). Each node may receive any number of broadcastedbeliefs 505 from any number of nodes within the communication range ofthe node receiving the beliefs.

After receiving the beliefs from nearby nodes, a node may begin theprocess of producing a converted belief (619). In the process ofproducing a converted belief, the location processor of a node mayprovide an estimation unit 507 configured to receive the transmittedbelief 501 in order to estimate an arbitrary signal metric of the nodein relation to at least one other node in the network. An arbitrarysignal metric is a signal metric that may be of any type or value (e.g.,the type of signal metric that may be used is not limited to symmetricalor homogenous signal metric). The estimated arbitrary signal metric 509may be a function of the received belief signal from the at least onenode.

It should be appreciated if an arbitrary signal metric has beenestimated from the same transmitting node from a prior iteration, orbefore the start of the localization process, a new arbitrary signal mayor may not be estimated. It should also be appreciated that the nodes inthe network may be heterogeneous such that the ARP estimates obtainedfrom the estimated signal metrics may use different types and/or valuesfrom node to node.

Furthermore, the nodes in the network may also be heterogeneous suchthat each node may use a different technique or process of estimatingthe signal metric. It should also be appreciated that the signal metricsand ARPs may be estimated through any combination of analytical,numerical, or statistical methods. It should also be appreciated thateach node may estimate signal metrics independently of the other nodesin the network. For example, node A may estimate signal metrics fromreceived messages without the knowledge of the estimated signal metricsof other nodes in the network.

Once an arbitrary signal metric 509 has been provided, this signalmetric may be sent to a converting unit 511 in order to provide aconverted belief 513 based on the estimated arbitrary signal metric 509.Each node in the network may have multiple estimating and convertingunits, or a calculating unit 515, that may be configured to determineconverted beliefs 513 for each of the received incoming beliefs 501simultaneously. Alternatively, the estimating and converting units, orcalculating unit 515, may be configured to determine converted beliefs513 for each of the received incoming beliefs 501 sequentially. Itshould be appreciated that a belief transmitted from another node may beused by a current node to estimate any number of signal metrics, whichmay, in turn, be used to describe any number of APRs.

The generation of the converted beliefs may be explained mathematicallywith the use of FIG. 4. For example, in the agent MS 1 the generation ofthe converted belief, resulting from a self-belief 421 from anchor BS 3being transmitted to agent MS 1, may be represented by the vertex 430.The SPA may be configured to send the generated converted belief,c_(3→1)(x) 432, to the equality vertex 431. Similarly, the transmittedself-belief 428 from the anchor BS 5 may be used to obtain estimatedarbitrary signal metrics to generate a converted belief, which isillustrated by the vertex 433. The converted belief, c_(5→1)(x₁) 434,may then also be sent to the equality vertex 431. Finally, thegeneration of a third converted belief, based on the transmittedself-belief 425, may be illustrated via the vertex 435. The generatedconverted belief, c_(2→1)(x₁) 436, may also be sent to the equalityvertex 431 via the SPA.

Likewise for the agent MS 2, converted belief generation may berepresented by vertices 441, 437, and 439, resulting in convertedbeliefs c₁₋₂(x₂) 442, c_(4→2)(x₂) 438, and c_(5→2)(x₂) 448,respectively. The converted beliefs c₁₋₂(x₂) 442, c_(4→2)(x₂) 438, andc_(5→2)(x₂) 448 may be sent to the equality vertex 431 of sub-graph MS 2via the SPA.

The generation of the converted beliefs may be represented by thefollowing equation:

c_(j→i)(x_(i))∝∫p(z_(j→i)|x_(i),x_(j))b^((L-1))(x_(j))dx_(j)

where c_(j→i)(x_(i)) is the converted belief generated in the currentnode i, p(z_(j→i)|x_(i),x_(j)) is the distribution of signal metricz_(j→i) extracted from the signal transmitted from node j to the currentnode i, and b^((L-1))(x_(j)) is the self-belief broadcasted by node j tothe current node i. The integration described above may be amulti-dimensional integration performed over for all possible x_(j). Itshould be appreciated that the generation of converted beliefs may beperformed through any combination of analytical, numerical, orstatistical integration methods. It should also be appreciated that theconverted belief may take into account any uncertainties associated withthe estimated signal metric z_(j→i) as a function of the locations x_(i)and x_(j), as well as any knowledge regarding the environment (e.g., thepresence of obstacles, low signal-to-noise ratio, weak signal strength,inherent uncertainty in the estimation process, etc.). It should beappreciated that the generation of the converted beliefs may beperformed through any combination of analytical, numerical, orstatistical methods.

Once all of the converted beliefs have been sent to the equalityvertices 431 of the sub-graphs of their respective nodes, the equalityvertex 431 may evaluate each converted belief. The equality vertex 431may self account for inconsistencies, such as if the value of onegenerated converted belief is significantly different from the othergenerated converted beliefs (621). For example, if a converted belief isthe result of a transmitted self-belief sent from a malfunctioning node,this self-belief may offset subsequent localization calculations. Themalfunctioning node may be a node transmitting signals with a lowsignal-to-noise ratio, a node transmitting false information, or a nodetransmitting information leading to a location estimation which variesgreatly from location estimations obtained by information transmittedfrom other nodes.

Once the converted beliefs have been evaluated for inconsistencies, anew self-belief for each node may be determined (623). The locationprocessor 500 of each node may forward all the generated convertedbeliefs 513 to a belief determining unit 517. The belief determiningunit 517 may generate a new determined self-belief 519 by taking intoaccount the marginal a priori distribution p(x_(i)) of the current nodeas well as the generated converted beliefs, which are a function of thetransmitted self-beliefs from the nodes in the network withincommunication range.

The generation of the new determined self-belief 519 may be performedwith taking a prior determined self-belief into account. In other words,the belief determining unit 517 may determine the new self-belief 519with memory, or knowledge, of a self-belief determined from a prioriteration. The determination of the new self-belief 519 with memory maybe represented as follows:

${b^{(L)}( x_{i} )} \propto {{b^{({L - 1})}( x_{i} )}{\prod\limits_{j \in \Gamma_{i}}{c_{j->i}( x_{i} )}}}$

where b^((L))(x_(i)) is the self-belief of node j being determinedduring the current iteration L, b^((L-1))(x_(i)) is the self-belief ofnode j determined in a prior iteration L−1, Γ_(i) is the set of theneighboring nodes of node i, from which node i has selected a consistentconverted belief, and c_(j→i)(x_(i)) is the converted belief generatedby signal metrics sent to node j from node i. It should be appreciatedthat the generation of the new determined self-belief 519 may also beperformed without memory or knowledge of a self-belief determined from aprior iteration. The new self-belief 519 generated without memory may berepresented as follows:

${b^{(L)}( x_{i} )} \propto {\prod\limits_{j \in \Gamma_{i}}{{c_{j->i}( x_{i} )}{p( x_{i} )}}}$

where p(x_(i)) is the marginal a priori distribution of the current nodei. Therefore, the marginal a priori distribution may be utilized insystems which do not take into account prior generated self-beliefs. Itshould be appreciated that the belief determining unit 517 may utilizeboth the marginal a priori distribution and the prior generatedself-belief of the current node i. It should also be appreciated thatthe nodes in the network may be heterogeneous in that some nodes maygenerate the new self-belief with memory, while other nodes in thenetwork may not use memory to generate the new self-belief. It shouldalso be appreciated that the generation of the new determinedself-beliefs 519, with or without memory, may be performed through anycombination of analytical, numerical, or statistical methods. It shouldbe appreciated that not every node in the network may need to generate aself-belief. For example, if it has been determined that a node has adefined location and has not moved since the prior iteration, that nodeneed not be update its self-belief.

Upon determining a new self-belief, an identifying unit 521 may be usedto estimate a self-location 523 (625). The estimated self-location 523may be determined by taking the mean, median, or mode, of the determinedself-belief, which may be in the form of a probability distribution. Thenodes in the network may utilize different methods of estimating theself-location 523.

The location processor 500 of the nodes in the network may also includea neighbor identifying unit 527. The neighbor identifying unit 527 maybe used to estimate a location of the nodes within communication rangeby taking the mean, median, or mode, of the beliefs transmitted by theneighboring nodes. Thus, a topological map 529 of the nodes in thenetwork may be created, giving each node knowledge of this topology.This may give every node in the network access to the complete networktopology. The topological map may be broadcast 531 by every node in aseparate data packet. It should be appreciated that the transmittedtopology 531 may also be comprised as part of the data packet comprisingthe transmitted determined self-belief 525. It should also beappreciated that the nodes in the network may employ different methodsof creating the topological map 529.

It should also be appreciated that the estimated signal metricsgenerated by each node may provide an ARP that may include informationon obstacles. Obstacles may exist between or around the nodes in thenetwork. Examples of obstacles may include, but are not limited to,walls or buildings. This information may also be used in generating atopology of the network and in generating self-beliefs.

The above mentioned operations 615-625 may be performed for all of thenodes in the network during the present iteration (627). Once theoperations 615-625 have been performed on all the nodes in the network,a new iteration may be performed (629). During the new iteration, thedetermined self-beliefs 517 may be updated to account for mobility (611)as previously described. Once updated, the updated determinedself-belief may then be broadcast 525, and the operations 615-625described above may then be performed using the updated determinedself-beliefs. It should be appreciated that not every node in thenetwork need be involved in all iterations. Furthermore, nodes may notparticipate in every computational operation involved in the iteration.

It should be understood that certain processes, such as the cooperativelocalization process, disclosed herein, may be implemented in hardware,firmware, or software. If implemented in software, the software may bestored on any form of computer readable medium, such as random accessmemory (RAM), read only memory (ROM), compact disk read only memory(CD-ROM), and so forth. In operation, a general purpose or applicationspecific processor loads and executes the software in a manner wellunderstood in the art.

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. A method for a wireless device to self identify a location in awireless network comprising: calculating at least one converted beliefrepresenting a distribution of at least one possible location of thewireless device within the wireless network, the at least one convertedbelief being a function of an arbitrary signal metric associated with awireless signal received by the wireless device from at least one otherwireless device; determining a self-belief as a function of the at leastonce converted belief; and identifying a self location, as a function ofthe self-belief, within the wireless network.
 2. The method of claim 1wherein a physical configuration of the wireless network is timevarying.
 3. The method of claim 1 wherein at least a subset of multipleother wireless devices is mobile relative to at least one other subsetof multiple other wireless devices.
 4. The method of claim 1 wherein atleast a subset of multiple other wireless devices estimates thearbitrary signal metric of a first type, and at least one other subsetof multiple other wireless devices estimates the arbitrary signal metricof a second type.
 5. The method of claim 4 wherein the arbitrary signalmetric is used to calculate a characteristic of the wireless device withrespect to the at least one other wireless device, the characteristicselected from the group consisting of: angle, distance, connectivity,position, pose, velocity, and angular velocity information.
 6. Themethod of claim 4 wherein the arbitrary signal of the first type isnon-symmetrical with respect to the arbitrary signal of the second type.7. The method of claim 1 wherein converting the at least one belief anddetermining the self-belief are based on factor graph principles.
 8. Themethod of claim 1 wherein converting the at least one belief furthercomprises self accounting for a capability related to estimating thearbitrary signal metric.
 9. The method of claim 1 wherein determiningthe self-belief further comprises updating a previously determinedself-belief or determining a new self-belief.
 10. The method of claim 1further comprising identifying a location of the at least one otherwireless device in the wireless network.
 11. The method of claim 1wherein the distribution is a probability density or a mass function.12. A location indicator apparatus for self identifying a location of awireless device in a wireless network, the apparatus comprising: acalculating unit configured to calculate at least one converted beliefrepresenting a distribution of at least one possible location of thewireless device within the wireless network, the at least one convertedbelief being a function of an arbitrary signal metric associated with awireless signal received by the wireless device from at least one otherwireless device; a self-belief determining unit configured to determinea self-belief as a function of the at least one converted belief; and anidentifying unit configured to identify a self location, as a functionof the self-belief, within the wireless network.
 13. The apparatus ofclaim 12 wherein a physical configuration of the wireless network istime varying.
 14. The apparatus of claim 12 wherein at least a subset ofmultiple other wireless devices is mobile relative to at least one othersubset of multiple other wireless devices, wherein the respectivecalculating, self-belief determining, and identifying units are furtherconfigured to account for mobility.
 15. The apparatus of claim 12wherein respective calculating units of at least a subset of multipleother wireless devices is configured to estimate the arbitrary signalmetric of a first type, and respective calculating units of at least oneother subset of the at least one other wireless device is configured toestimate the arbitrary signal metric of a second type.
 16. The apparatusof claim 15 wherein the arbitrary signal metric measures acharacteristic of the wireless device with respect to the at least oneother wireless device, the characteristic selected from the groupconsisting of: angle, distance, connectivity, position, pose, velocity,and angular velocity information.
 17. The apparatus of claim 15 whereinthe arbitrary signal of the first type is non-symmetrical with respectto the arbitrary signal of the second type.
 18. The apparatus of claim12 wherein the converting unit is further configured to convert the atleast one belief, and the determining unit is configured to determinethe self-belief, based on factor graph principles.
 19. The apparatus ofclaim 12 wherein the calculating unit is further configured to selfaccount for a capability related to estimating the arbitrary signalmetric.
 20. The apparatus of claim 12 wherein the determining unit isfurther configured to update a previously determined self-belief ordetermine a new self-belief.
 21. The apparatus of claim 12 theidentifying unit is further configured to identify a location of atleast one other wireless device in the wireless network.
 22. Theapparatus of claim 12 wherein the distribution is a probability densityor a mass function.
 23. A computer program product having a computerprogram stored thereon, the computer program defined by instructionswhich, when executed by a processor in a wireless device in a wirelessnetwork, cause the processor to: calculate at least one converted beliefrepresenting a distribution of at least one possible location of thewireless device within the wireless network, the at least one convertedbelief being a function of an arbitrary signal metric associated with awireless signal received by the wireless device from at least one otherwireless device; determine a self-belief as a function of the at leastonce converted belief; and identify a self location, as a function ofthe self-belief, within the wireless network.